By Raj Sattaluri, SVP – Solutions Engineering, Innominds
Modernisation as the defining imperative
Every organisation today is being judged by its ability to modernise — not by competitors, not by customers, but by its own systems. Legacy platforms have become the most persistent source of delay, cost, and operational risk. Decades of embedded business rules, patches, and exception logic lie buried in opaque code that engineering teams lack the time or clarity to interpret. For large enterprises, particularly those with complex, decades-old core banking or manufacturing systems, this architectural debt is no longer a balance sheet item, it is a competitive liability.
The organisations moving ahead are the ones treating modernisation as a strategic reset: a chance to rebuild the foundations of product delivery, data flow, and AI-enabled operations. AI-led engineering and Agentic AI are changing the equation, enabling teams to uncover logic, map dependencies, and redesign systems with precision. Modernisation is a knowledge-driven, insight-led transformation that directly drives speed, resilience, and innovation.
The dual lens: ISVs and enterprises
Independent Software Vendors (ISVs) and enterprises may share the goal of modernisation, but their drivers and approaches diverge sharply.
ISVs modernise to preserve product relevance and competitiveness, and faster release cycles, maintainable architectures, and high-quality code determine market success. For these product-centric organisations, the objective is often a significant gain in agility and speed, targeting 2 to 3 times acceleration in feature releases and aim for a 30% reduction in technical debt to stay competitive. As a result, AI copilots and agentic systems are increasingly embedded throughout the SDLC to accelerate analysis, testing, documentation, and refactoring.
Enterprise modernisation, by contrast, is less about product and more about applications, focusing on operational continuity and resilience. Large organisations must unravel decades of business logic and mission-critical workflows locked within massive application estates. Modernisation for enterprises is therefore incremental, data-driven, and heavily reliant on domain knowledge. Today’s leading enterprises operate with a continuous transformation model powered by AI-enhanced insights and data intelligence.
The knowledge challenge: Extracting logic hidden in legacy systems
Legacy systems are not just aging code; they constitute an organisation’s most valuable institutional memory. Business rules, financial data flows, and complex decision paths are often buried in opaque, interdependent modules, making modernisation a high-risk endeavor. Engineering teams cannot act confidently without knowing the “why” behind every system behaviour. Modernisation struggles almost always stem from an incomplete understanding of what the existing system actually does.
AI-powered code-intelligence tools are fundamentally changing this dynamic. They move from merely analysing syntax to understanding semantics, uncovering business rules, dependency chains, and hidden logic at scale. This semantic understanding is emerging as the key differentiator in reducing modernisation risk.
Organisations that envision modernisation as a knowledge extraction exercise, rather than a speculative rewrite, make concrete progress and avoid costly errors. This process relies heavily on a specialised data engineering layer to accurately transform business logic extracted by AI into validated, modern data models and microservices blueprints.
AI agents: The contextual copilots of modern engineering
A new class of AI is transforming modernisation: agentic, context-aware systems that act as digital copilots for engineering teams. These agents draw on historical code repositories, system execution logs, architecture diagrams, and runtime data to understand system intent. They generate test cases, propose refactorings, and help validate impact across dependent systems, all while engineers retain final oversight.
AI agents amplify human capability. While governance requires attention, particularly around IP protection and validation for hallucination, agentic AI delivers speed, clarity, and contextual insight that were previously unattainable. The breakthrough is agents that understand intent—the reasoning behind the code—not just the code itself, improving the baseline quality of engineering decisions and making modernisation less episodic and more continuous.
Driving modernisation with AI
Modernisation initiatives have long been held back by high manual effort and a reliance on scarce domain experts. As engineering teams embrace next-generation AI, the paradigm is shifting toward a scalable hybrid model:
- Reverse engineering: Deploy AI agents to automatically capture business logic and translate it into clearly defined user stories and functional specifications.
- Validation and testing: Agents examine test coverage to highlight gaps before refactoring begins, enabling teams to plan functional-equivalence testing with confidence. AI then generates the necessary unit, API, and regression scripts.
- Data alignment: For complex data migration, agent-driven mapping supports schema alignment, validation, reconciliation, and optimisation, eliminating manual, error-prone tasks.
Crucially, human oversight remains the final cognitive layer—reviewing AI outputs to maintain architectural integrity and business alignment, accelerating modernisation while strictly controlling risk.
From cost optimisation to innovation enablement
Modernisation has outgrown its cost-saving narrative. It is now a driver of innovation and growth, effectively unlocking rapid experimentation, improving system reliability, and freedom to build for new digital business models.
For ISVs, modernisation delivers modular architectures, faster release cycles, and cleaner codebases to enable innovation. For enterprises, it means stable migration paths, reduced risk, and greater return on their digital investments. Innovation today is built into the modernisation strategy rather than being an afterthought, and the convergence of AI and platform modernisation is central to reinvention.
The next era of modernisation will be continuous, not episodic, and built on knowledge, not just code. AI agents, semantic intelligence systems, and human engineers will collaborate to interpret legacy systems, plan robust transformation paths, and validate each step of modernisation in real time.
The future of engineering is not about rewriting the past; it’s about using AI to semantically map the logic, making knowledge the most potent driver of the modernisation flywheel.